Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction

TL;DR

The paper investigates self-supervised speech representation learning using Barlow Twins (BT), focusing on invariance and redundancy reduction as core inductive priors. It provides an empirical analysis of BT's downstream transfer, potential for disentanglement, and the effects of loss-variant ablations, showing that BT affords strong transfer with high-quality upstream data but falls short on fine-grained factorization of latent factors. The study demonstrates generalization across tasks and domain shifts, and highlights that dataset quality can outperform sheer size in driving transfer, while disentanglement remains a key challenge. It concludes by outlining pathways to improve BT via perceptual priors and additional inductive biases to move toward more hierarchical, decoupled representations for speech.

Abstract

Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are transferable to downstream tasks. This study provides an empirical analysis of Barlow Twins (BT), an SSL technique inspired by theories of redundancy reduction in human perception. On downstream tasks, BT representations accelerated learning and transferred across domains. However, limitations exist in disentangling key explanatory factors, with redundancy reduction and invariance alone insufficient for factorization of learned latents into modular, compact, and informative codes. Our ablations study isolated gains from invariance constraints, but the gains were context-dependent. Overall, this work substantiates the potential of Barlow Twins for sample-efficient speech encoding. However, challenges remain in achieving fully hierarchical representations. The analysis methodology and insights pave a path for extensions incorporating further inductive priors and perceptual principles to further enhance the BT self-supervision framework.

Figures (5)

• Figure S1: The BT framework for learning invariant speech representations. Stage 1: An encoder $f_\theta$ process augmented views $X^A$ and $X^B$ of the same speech input $X$ and project them into a shared latent space. The BT loss (Equation \ref{['eq:BT']}) enforces redundancy reduction between latents from different samples while maximizing correlation for positive pairs (two views of the same sample). This causes the encoders to produce invariant representations capturing speaker identity while reducing sensitivity to augmentations. Stage 2: The learned latent representations $Z^A$ and $Z^B$ can then be used for downstream speech processing tasks to evaluate the model's generalization capability.
• Figure S2: (Left column) View 1 provides a dual representation, featuring the time-domain signal (top row) and its corresponding time-frequency spectrogram (second row), both derived from the first perturbed version of the original audio signal. (Right column) View 2 presents a similar pair of representations. The higher harmonic partials present in the first view are not visibly present in the second view, however, the underlying information content remains invariant.
• Figure S3: Represent the empirical cross-correlation matrices, contrasting the untrained state (left) with the trained state (right) within the BT framework. These matrices visually represent the relationships between different views of the same speech input for the current mini-batch. The comparison allows us to observe the transformation in cross-correlation patterns following the self-supervised learning process, highlighting the model's ability to capture invariance (higher correlation of diagonal elements of the trained network's matrix) and de-correlation of off-diagonal elements.
• Figure S4: (a) Top-1 Accuracy for Speaker Recognition, comparing five base models over 50 experimental runs, highlighting the performance and stability of these techniques. (b) Top-1 Accuracy for Gender Recognition from speech, using the same base models, which shows a similar performance trend, indicating task-specific model effectiveness and the nuanced nature of gender features in speech data.
• Figure S5: (a) Boxplot of Top-1 Accuracy in Emotion Recognition across five different base models over 50 experimental runs, showing the consistency and variability in model performances. (b) Boxplot of Top-1 Accuracy in a Keyword Spotting Task for the same base models and number of runs, illustrating the impact of model architecture on task-specific accuracy.